A dynamic hierarchical Bayesian model for the estimation of day-to-day origin-destination flows in transportation networks
From MaRDI portal
Publication:2005933
DOI10.1007/s11067-019-09490-5OpenAlexW2997012473WikidataQ126394812 ScholiaQ126394812MaRDI QIDQ2005933
Luís Carvalho, Carlos Felipe Grangeiro Loureiro, Anselmo R. Pitombeira-Neto
Publication date: 8 October 2020
Published in: Networks and Spatial Economics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s11067-019-09490-5
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- An excess-demand dynamic traffic assignment approach for inferring origin-destination trip matrices
- Bayesian forecasting and dynamic models.
- Time dependent origin-destination estimation from traffic count without prior information
- Transportation systems analysis. Models and applications.
- Traffic matrix inference in IP networks
- Path flow and trip matrix estimation using link flow density
- Path flow estimator in an entropy model using a nonlinear L-shaped algorithm
- A relaxation approach for estimating origin-destination trip tables
- Network Tomography: Estimating Source-Destination Traffic Intensities from Link Data
- Estimation of Origin–Destination Trip Rates in Leicester
- Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
- Bayesian Inference on Network Traffic Using Link Count Data
- Dynamic Estimators of Origin-Destination Matrices Using Traffic Counts
- DATA AUGMENTATION AND DYNAMIC LINEAR MODELS
- On Gibbs sampling for state space models
- Estimation and Prediction of Time-Dependent Origin-Destination Flows with a Stochastic Mapping to Path Flows and Link Flows
- Fixed Point Approaches to the Estimation of O/D Matrices Using Traffic Counts on Congested Networks
- Time-Varying Network Tomography: Router Link Data
- Estimating Latent Processes on a Network From Indirect Measurements
- Identifiability of flow distributions from link measurements with applications to computer networks
- Monte Carlo sampling methods using Markov chains and their applications
This page was built for publication: A dynamic hierarchical Bayesian model for the estimation of day-to-day origin-destination flows in transportation networks
